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Received: 13 April 2020 Accepted: 11 August 2020 DOI: 10.1111/gcb.15336
PRIMARY RESEARCH ARTICLES
Global variation in diurnal asymmetry in temperature, cloud cover, specific humidity and precipitation and its association with leaf area index Daniel T. C. Cox
| Ilya M. D. Maclean
Environment and Sustainability Institute, University of Exeter, Penryn, UK Correspondence Daniel T. C. Cox, Environment and Sustainability Institute, University of Exeter, Penryn, Cornwall TR10 9FE, UK. Email:
[email protected] Funding information Natural Environment Research Council, Grant/Award Number: NE/P01156X/1 and NE/P01229/1
| Alexandra S. Gardner
| Kevin J. Gaston
Abstract The impacts of the changing climate on the biological world vary across latitudes, habitats and spatial scales. By contrast, the time of day at which these changes are occurring has received relatively little attention. As biologically significant organismal activities often occur at particular times of day, any asymmetry in the rate of change between the daytime and night-time will skew the climatic pressures placed on them, and this could have profound impacts on the natural world. Here we determine global spatial variation in the difference in the mean annual rate at which near-surface daytime maximum and night-time minimum temperatures and mean daytime and mean night-time cloud cover, specific humidity and precipitation have changed over land. For the years 1983–2017, we derived hourly climate data and assigned each hour as occurring during daylight or darkness. In regions that showed warming asymmetry of >0.5°C (equivalent to mean surface temperature warming during the 20th century) we investigated corresponding changes in cloud cover, specific humidity and precipitation. We then examined the proportional change in leaf area index (LAI) as one potential biological response to diel warming asymmetry. We demonstrate that where night-time temperatures increased by >0.5°C more than daytime temperatures, cloud cover, specific humidity and precipitation increased. Conversely, where daytime temperatures increased by >0.5°C more than night-time temperatures, cloud cover, specific humidity and precipitation decreased. Driven primarily by increased cloud cover resulting in a dampening of daytime temperatures, over twice the area of land has experienced night-time warming by >0.25°C more than daytime warming, and has become wetter, with important consequences for plant phenology and species interactions. Conversely, greater daytime relative to night-time warming is associated with hotter, drier conditions, increasing species vulnerability to heat stress and water budgets. This was demonstrated by a divergent response of LAI to warming asymmetry. KEYWORDS
activity patterns, climate change asymmetry, daytime warming, leaf area index, night-time warming, vegetation cover, warming asymmetry, water cycle
This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited. © 2020 The Authors. Global Change Biology published by John Wiley & Sons Ltd Glob. Change Biol.. 2020;00:1–13.
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1 | I NTRO D U C TI O N
COX et al.
Rising temperatures are associated with increased evapotranspiration that has been attributed to global trends of increasing cloud cover
Human activities are altering the planet's climate, with biologi-
and near-surface humidity and precipitation (e.g. Huntington, 2006;
cally meaningful changes taking place from the macro- (IPCC,
Ohmura & Wild, 2002), further influencing daily temperature
2013) to the microscale (e.g. Maclean, Suggitt, Wilson, Duffy, &
ranges (e.g. Dai et al., 1999; Du, Wu, Jin, Zong, & Meng, 2013; Zhou
Bennie, 2017; Suggitt et al., 2018). Anthropogenic increases in
et al., 2015). Because a warmer atmosphere is able to hold more
atmospheric CO2 and other greenhouse gases have resulted in
moisture, one might predict that asymmetric warming may impact
a strong global trend of increasing maximum and minimum tem-
cloud cover, specific humidity and precipitation differently at differ-
peratures (IPCC, 2013), which in turn has driven an intensifica-
ent times of the day. Consequently, in regions where the night-time
tion of the hydrological cycle (e.g. Huntington, 2006; Ohmura
is warming faster than the daytime we may expect to see greater rel-
& Wild, 2002). The rates and directions in which these changes
ative increases in night-time specific humidity than daytime specific
are occurring have been shown to vary with land use and land
humidity owing to the temperature-dependent limits on how much
cover change (e.g. Pielke, 2005), across elevations (e.g. Rangwala,
water vapour a given volume of air can hold (Gaffen & Ross, 1999;
Sinsky, & Miller, 2013), ecoregions (e.g. Beaumont et al., 2011;
Wang & Gaffen, 2001). Conversely, relatively higher temperatures
Zhou, Chen, & Dai, 2015) and latitudes (e.g. Deutsch et al., 2008),
during the daytime potentially enable more moisture to be held in the
and the associated impacts of these changes on the natural
atmosphere, which can lead to increased levels of specific humidity
world have been diverse (e.g. Maclean & Wilson, 2011; Walther
and precipitation during the daytime.
et al., 2002). By contrast with the rich literature examining spa-
As biological activities and processes often occur at partic-
tial variation in climate change, that exploring temporal varia-
ular times of day, diel asymmetries in warming, cloud cover, hu-
tion focuses principally on changes to daily, monthly, or annual
midity or precipitation are likely to have diverse and profound
means. Variation in the rates at which these changes are occur-
consequences for the natural world. In plants, for example, rising
ring across the diel (daily) cycle has received surprisingly little at-
minimum night-time temperatures are expected to affect carbon
tention. However, if the climate is not changing at a consistent
assimilation and consumption, because most photosynthesis oc-
rate during both the daytime and night-time, then the use of daily
curs during the daytime and is more sensitive to maximum daytime
means will lead to underestimation or overestimation of changes
temperatures, whereas respiration occurs throughout the diel cycle
across a particular dimension of the day. Given that many species
(Atkin et al., 2013) and is influenced by both daytime maximum and
show strong time partitioning in their daily biological processes
night-time minimum temperatures (Peng et al., 2013; Peraudeau
(e.g. plant photosynthesis; Xu, Medvigy, Powers, Becknell, &
et al., 2015; Xia et al., 2014). Night-time warming has been found to
Guan, 2016) or activity patterns (Bennie, Duffy, Inger, & Gaston,
affect vegetation growth (Alward, Detling, & Milchunas, 1999; Peng
2014), neglecting sub-daily changes in climate could have import-
et al., 2004, 2013; Xia et al., 2014), however, responses of vegetation
ant consequences for predicting the impacts of climate change on
to diel asymmetric warming differ between regions and ecosystems
global biodiversity.
(Alward et al., 1999; Beier et al., 2008; Peng et al., 2004, 2013; Wan,
There has been a global trend of night-time temperatures in-
Xia, Liu, & Niu, 2009) and large-scale responses are less clear. In the
creasing at a faster rate than daytime temperatures (Davy, Esau,
Northern Hemisphere, positive correlations have been observed
Chernokulsky, Outten, & Zilitinkevich, 2017; Easterling et al., 1997;
between daytime maximum temperatures and vegetation growth in
Karl et al., 1991; Vose, Easterling, & Gleason, 2005), which has led to
wet and cool ecosystems, but negative correlations in dry temperate
a shrinking of the diel temperature range (e.g. Vose et al., 2005). This
regions (Peng et al., 2013). However, global trends in changing veg-
diel asymmetry in warming varies spatially, with some regions also
etation cover in response to warming asymmetry are not currently
experiencing daytime temperatures increasing faster than night-time
known.
temperatures (Donat & Alexander, 2012; Easterling et al., 1997). The
Studies to date investigating diel asymmetry in warming have
downward trend in daily temperature ranges is thought ultimately to
been hampered by incomplete spatial coverage (e.g. Easterling
be related to the upward trend in cloud cover over land (Dai, Genio,
et al., 1997; Karl et al., 1993; Vose et al., 2005) or have tended to focus
& Fung, 1997; Dai, Trenberth, & Karl, 1999; Sun, Groisman, Bradley,
on a specific hemisphere (e.g. Davy et al., 2017; Peng et al., 2013) or
& Keimig, 2000), with clouds having a strong and opposing effect on
region (e.g. Kuang & Jiao, 2016). Moreover, it is not known whether
temperature, dependent on the time of day (Sun et al., 2000). During
diel warming asymmetry is matched by diurnal asymmetry in other
the daytime, clouds attenuate shortwave radiation resulting in a low-
climate variables such as cloud cover, specific humidity or precipita-
ering of daytime temperatures, whilst at night they re-emit longwave
tion. Globally, it is currently unclear how diurnal asymmetry in the
radiation downward, warming the earth's surface (Dai et al., 1999).
changing climate varies spatially, where the extreme differences be-
Warming asymmetry has also been attributed to soil moisture
tween daytime and night-time warming are taking place, and what
(Dai et al., 1999), precipitation (Dai et al., 1997; Zhou et al., 2009),
proportion of the land's surface is undergoing different rates of
boundary layer depth (Davy et al., 2017) and at smaller spatial
warming.
scales, anthropogenic land forcing (e.g. Chen & Dirmeyer, 2019; Lee et al., 2011).
Here we use a 35 year global data set for near-surface temperature, cloud cover, specific humidity and precipitation to examine: (a)
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COX et al.
associations between the long-term trend in mean daily cloud cover
period. Finally, to avoid giving undue weighting to pixels at high
and the difference in the rate of change between daytime maximum
latitudes for each year, we reprojected the rasters for each climate
temperatures and night-time minimum temperatures; (b) spatial vari-
variable to the Behrmann equal area projection (181 × 243 km res-
ation in the difference in the rate of change between mean daytime
olution; EASE-Grid 2.0: EPSG:6933; Brodzik, Billingsley, Haran,
and mean night-time cloud cover, specific humidity and precipita-
Raup, & Savoie, 2012).
tion; (c) the relationship between warming asymmetry and diurnal asymmetry in cloud cover, specific humidity and precipitation in regions that have experienced a greater increase of >0.5°C in the
2.1 | Daytime and night-time climate trends
daytime or the night-time; and (d) climate change asymmetry in three case study regions that display high degrees of warming asymmetry,
In each terrestrial pixel for every 24 hr cycle, we determined the
namely the Tibetan Plateau, West Africa and East Africa. Finally, (e)
maximum daytime temperature and the minimum night-time tem-
we examine one potential biological response to diel warming asym-
perature, before taking the mean across each year. We focused on
metry, change over recent decades in vegetation cover (measured
daytime maximum and night-time minimum temperature, because
through the leaf area index; LAI).
we were interested in the ecological impacts of changing extremes of daytime and night-time temperature variation that have
2 | M E TH O DS
been shown to have greater ecological impacts than temperature means (Ma, Hoffmann, & Ma, 2015). Unlike temperature, cloud cover, specific humidity and precipitation do not follow a similar
We used NCEP Reanalysis 2 climate data provided by the NOAA/
consistent daily cycle, and so we examined long-term trends in an-
OAR/ESRL PSD (https://www.esrl.noaa.gov/psd/). This is a global
nual mean daytime and annual mean night-time measures. Higher
gridded data set of modelled forecasts and hindcasts calibrated
latitudes experience 24 hr daylight and darkness and so diel asym-
against observed data and is the best long-term reanalysis data
metry is conflated with seasonal asymmetry, and we therefore
set currently available. All spatial manipulations were performed
excluded 24 hr periods that were solely day or night. To obtain
in QGIS v3.4 (QGIS Development Team, 2018) and R software for
a global average for each climate variable in each year for the day-
statistical computing v3.5.2 (R Core Team, 2018). We used the
time and again for the night-time we calculated the mean value
R packages ‘microclima’ (Maclean, Mosedale, & Bennie, 2019)
across pixels. We then built a linear model of the annual global
for data preparation and ‘raster’ (Hijmans, 2019) for raster
anomaly relative to the 35 year mean for daytime and night-time
manipulation.
(response) modelled first against year (0–34) and then against
We downloaded near-surface 6 hourly observations for tem-
the interaction between year and a binary factor of daytime and
perature (°C) at 2 m above ground level, cloud cover (%), specific
night-time. As a measure of the level of variation explained by the
humidity (g/m3) and the precipitation rate at the surface (mm). We
linear regression, for each model we also calculated McFadden's
used specific humidity instead of relative humidity to avoid con-
pseudo R-squared.
flation with temperature. Data were from 1983 to 2017 and re-
To understand the spatial distribution of diel warming asym-
corded at 1.904 × 1.875 degree resolution. The data set covered
metry, for each year for the daytime and again for the night-time,
from 89.5°N to 89.5°S and was projected using WGS84. A spline
we carried out linear regression across years for each pixel before
interpolation was then used to produce hourly climate estimates
multiplying the slope by 35 years to calculate the absolute level
for cloud cover and specific humidity. For temperature, a more
of warming between 1983 and 2017. To calculate the difference
sophisticated interpolation was used in which this is assumed to
in the rate of change between the daytime and the night-time,
follow a sinusoidal diurnal cycle, with the periodicity determined
we subtracted the daytime change from the night-time change.
by sunrise and sunset. Final values for temperature were then cal-
We then repeated this process for cloud cover, specific humidity
culated using the ‘hourlytemp’ function in the R package ‘micro-
and precipitation. To capture the area of land experiencing greater
clima’ where deviations from this cycle are determined by daytime
change in the daytime or the night-time, we calculated the per-
solar radiation and night-time sky emissivity. The ‘suntimes’ func-
centage of land that crossed an arbitrary asymmetry threshold,
tion provided the time of sunrise and sunset for the centroid of
namely temperature >0.25°C, cloud cover >2.5%, specific humid-
each pixel, allowing a binary raster for each hour of the day to be
ity >0.1 g/m3 and precipitation >0.1 mm. Finally, we plotted the
generated showing whether this was during the daytime or night-
daytime and night-time annual global anomalies against the long-
time (rounded to the nearest hour). The ocean exerts different
term means.
pressures on the climate compared to the land (Sutton, Suckling,
Cloud cover has a strong influence on surface solar heating and
& Hawkins, 2015), therefore for all climate variables we masked
upward longwave radiation (Dai et al., 1997; Sun et al., 2000), there-
pixels where the ocean covered the centroid of the pixel. We also
fore we examined the relationship between mean daily cloud cover
excluded small islands. For precipitation, for which estimates are
and the temperature change difference between day and night. For
provided over a 6 hr period, amounts were partitioned accord-
each pixel and year as above, we calculated the mean daily cloud
ing to whether more than half of day or night fell within the 6 hr
cover before carrying out linear regression across years for each
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COX et al.
pixel to determine the level of cloud cover change between 1983
LAI values was downloaded from the National Climatic Data Center
and 2017. We then calculated Pearson's correlation between the diel
(Vermote & NOAA CDR Program, 2019). Values were then aver-
temperature change difference and (a) mean daily cloud cover, (b)
aged for each month. Missing values from regions obscured by cloud
diel cloud clover change difference, (c) diel specific humidity change
cover, or not obtained due to adverse data capture conditions were
difference, and (d) diel precipitation change difference.
imputed as follows. First, the entire globe was divided into 100,
To explore the relationship between diel warming asymme-
36° × 18° regions. Within each region mean values for each month
try and diurnal differences in the rate of change of cloud cover,
were calculated, and all grid cells values were divided by the mean
specific humidity and precipitation, for each climate variable
monthly value. Missing values were then approximated by spline
we selected all pixels that experienced: (a) night-time minimum
interpolation on the time series for each grid cell, before multiply-
warming of >0.5°C more than the daytime maximum, and (b) day-
ing by the mean monthly value to add the seasonal effect back in.
time maximum warming of >0.5°C more than the night-time mini-
This, in effect, removes the season effect before interpolation so
mum (0.5°C being the level that the mean surface air temperature
as to ensure that if missing values coincided with seasonal peaks or
warmed during the 20th century; IPCC, 2013). For the daytime
troughs in LAI, values are not over- or underestimated. For a small
and night-time we then plotted the global anomaly against the
number of locations with a high proportion of missing data, LAI val-
long-term mean.
ues could not be estimated in this way. For these locations, a land
To understand the relationship between warming asymmetry
cover type was identified using a data set from the European Space
and diel asymmetry in cloud cover, specific humidity and precipita-
Agency (Lamarche et al., 2017). Grid cells were then assigned the
tion at smaller spatial scales, which may be influenced by different
mean monthly value of LAI for that land cover type, estimated using
processes and pressures, we examined climate asymmetry in three
non-missing data and separately for each 36 × 18° region.
case study regions that displayed high levels of warming asymme-
Data were aggregated in annual composites of mean LAI, be-
try (e.g. Pingale, Adamowski, Jat, & Khare, 2015): (a) the Tibetan
fore being reprojected to the Behrmann equal area projection and
Plateau, a high altitude site that has experienced high levels of night-
resampled to match the resolution of the equal area climate layers
time warming and has been the focus of intense climate research;
(181 × 243 km). For each land pixel, we carried out linear regression
and two tropical regions that experienced asymmetry in, respec-
across years before calculating the total loge proportional change in
tively, the night-time (b) West Africa, and the daytime (c) East Africa.
LAI between 1983 and 2017. We then modelled the loge propor-
At each site for the daytime and the night-time we plotted the global
tional change in LAI (response) against the interaction between the
anomaly against the long-term mean.
absolute change in total annual precipitation and a binary factor of whether warming occurred more during the daytime or the nighttime. The absolute change in precipitation in each pixel was deter-
2.2 | The response of vegetation cover to warming asymmetry
mined as the linear regression of the total annual precipitation in each pixel multiplied by 35 years.
To examine a biological response to the 35 year change in warming asymmetry, we quantified relationships with the loge propor-
3 | R E S U LT S
tional change in LAI. LAI is the total one-sided leaf area per unit ground area and therefore a strong indicator of volumetric biomass
Global trends did not reveal diurnal asymmetry in the annual rate
within an ecosystem and is a critical variable scaling photosynthe-
at which daytime maximum and night-time minimum near-surface
sis, respiration and evapotranspiration (Asner, Braswell, Schimel, &
temperatures, and mean daytime and mean night-time cloud cover,
Wessman, 1998; Boussetta, Balsamo, Beljaars, Kral, & Jarlan, 2013;
specific humidity and precipitation have changed over land be-
Jarlan et al., 2008). A daily 0.05° gridded data set (1983–2017) of
tween 1983 and 2017 (Table 1; Figures 1 and 2). However, there
Variable
Dimension
Temperature (°C)
Year
Coefficient
Total change
0.029 (0.002)***
1.02
Day/Night
0.004 (0.004)
0.14
Cloud cover (%)
Year
6.9e-3 (0.005)
0.24
Specific humidity (g/m3)
Year
Precipitation (mm/6 hr)
Year
0.0014 (2.5e-4)**
Day/Night
−2.9e-4 (5.2e-4)
Day/Night Day/Night
−6.4e-4 (0.01)
−0.02
0.013 (0.0013)*** −0.0013 (0.0026)
2
pR2
.72 .12
0.46 0.046
.59
0.05 −0.01
Note: Statistical significance is shown as: **0.5°C more during the daytime than the night-time expe-
cover has decreased showing a trend of greater daytime warming
rienced reduced levels of cloud cover, specific humidity and precipita-
(Figure 1).
tion in both the daytime and night-time (Table 3b; Figure 3b).
Over twice the area of land has experienced increased warming
At smaller spatial scales, the Tibetan Plateau has experienced
at night of >0.25°C relative to the daytime, whereas similar areas
median night-time minimum temperature increases of 1.2°C
of land saw changes in cloud cover of >2.5% across the daytime or
(range: 0.21°C; 3.44°C) more than daytime maximum tempera-
the night-time (Table 2; Figures 1 and 2). Globally, over twice the
tures, and this has been accompanied by increases in daytime
area of land has experienced increased specific humidity >0.1 g/m3
and even greater increases in night-time cloud cover (Table 3c;
during the daytime than the night-time (Table 2; Figure 1), whilst in-
Figure 4a). We found that specific humidity increased more in the
creased rainfall of >0.1 mm/6 hr during the daytime has been more
daytime than the night-time, and there was some evidence that
common than during the night-time. Although statistically signif-
night-time precipitation has increased more quickly than daytime
icant, temperature change difference was less strongly correlated
precipitation (Table 3c; Figure 4a). In West Africa there has been
with cloud cover change difference and precipitation change dif-
a median temperature change difference of night-time minimum
ference (Pearson's correlation 0.25°C in night-time minimum temperatures
changing climate. We examined one possible biological response to
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COX et al.
warming asymmetry, in the relationship between precipitation and
Wu, 2006; Hua et al., 2018). The Plateau has also experienced diel
vegetation growth. Night-time warming was associated with a wet-
asymmetry in the other climate variables, with increased cloud cover
ting of the climate. Thus, in regions where there was a greater in-
and some evidence of increased specific humidity at night, whilst
crease in precipitation, the associated increase in cloud cover and
precipitation is greater during the daytime. The Plateau has been
reduction in daytime maximum temperatures and available sunlight
the focus of research on the ecological impacts of warming asym-
for photosynthesis is likely to have driven the negative associa-
metry, primarily related to plant growth, and these impacts have
tion between rainfall and vegetation growth. Conversely, daytime
been found to be diverse, from increased vegetation growth (Xia
warming was associated with a drying of the climate, and so here it
et al., 2018), shifting phenology (Liu, Yin, Shao, & Qin, 2006; Meng
is likely that water availability is the constraining factor, resulting in
et al., 2019; Suonan, Classen, Zhang, & He, 2017), to reduced crop
a positive relationship between vegetation growth and rainfall. The
yields (Peng et al., 2004), soil respiration (Zhong, Zhang, & Zhang,
inherent asymmetry in change complicates predictions of ecological
2019) and nectar yields (Mu et al., 2015). However, ecological stud-
responses, especially in complex systems. For example, global trends
ies have yet to consider the impacts of diel asymmetry in other as-
in LAI are also strongly influenced by other factors, such as land use
pects of the changing climate.
change (Hardwick et al., 2015) and local levels of CO2 (Dermody,
The biological impacts of diel asymmetry on climate are not solely
Long, & DeLucia, 2006; Li et al., 2018). However, their inclusion was
dependent on the magnitude of change, but also on the latitude at
outside of the scope of this study though likely accounts for the low
which change is taking place. In tropical regions, for example, spe-
explanatory power of the model (pR 2 = .03).
cies have evolved under relatively constant and stable climatic con-
Warming asymmetry has also been found to impact the be-
ditions (Janzen, 1967; Sheldon, Huey, Kaspari, & Sanders, 2018), and
haviour and physiology of ectotherms leading to impacts on individ-
live closer to their thermal optimum. As such, even a small tempera-
ual fitness, species interactions and ecosystem processes (Barton
ture increase can have a large impact on their physiology, behaviour
& Schmitz, 2018; Speights & Barton, 2019). Regardless of whether
and population sizes (Deutsch et al., 2008). Projections suggest that
faster warming has occurred during the daytime or night-time, re-
even with 0.25°C, and we provide further support that this is driven
>3°C between night-time minimum and daytime maximum tempera-
primarily by changing levels of cloud cover and is associated with a
tures (Table 3c; Figure 4a). The Plateau has an average elevation of
wetting (increased night-time warming) and drying (increased daytime
4,500 m, being the highest and largest highland in the world and
warming) of the climate. Levels of night-time warming were similar
rates of warming and cloud cover have been found to be significantly
regardless of whether there was greater daytime or night-time warm-
amplified by elevation, particularly during the night-time (Duan &
ing, indicating that increased night-time warming is largely driven by
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COX et al.
increased cloud cover damping daytime temperatures as opposed to raising night-time temperatures (see also Dai et al., 1999). Increased night-time warming was more prevalent in wetter regions, where increased cloud cover reduces photosynthesis and drives a negative correlation between precipitation and vegetation growth. Increased daytime warming was associated with drier regions, where water availability may be the limiting factor on vegetation growth and results in a positive correlation between precipitation and vegetation growth. Diel asymmetry in how the climate is changing is likely to have major implications for temperature and water-dependent ecological processes, impacting species through thermoregulatory and water budgets. The broader implications for ectotherms and endotherms are likely to be profound. By considering climate change spatially and temporally, over the diel cycle, we can more accurately assess the climate threat posed to species, especially for those that show distinct time partitioning in biologically meaningful activities and processes. AC K N OW L E D G E M E N T S K.J.G. was supported by a Natural Environment Research Council (NERC) grant NE/P01156X/1. A.S.G. was funded by an NERC grant NE/P01229/1 with support from the Cornwall Council. C O N FL I C T O F I N T E R E S T The authors declare no conflict of interest. AU T H O R C O N T R I B U T I O N D.T.C.C., I.M.D.M. and K.J.G. conceived and designed the study. I.M.D.M. generated the hourly climate data and the LAI layer. D.T.C.C., I.M.D.M. and A.S.G. analysed the data. D.T.C.C. and A.S.G. wrote the paper. All authors contributed critically to the drafts and gave final approval for publication of the paper. This research has not been previously presented elsewhere. DATA AVA I L A B I L I T Y S TAT E M E N T Data derived from public domain resources. The NCEP_Reanalysis 2 data used in this study are available from NOAA/OAR/ESRL PSD, Boulder, Colorado, United States, from their website https://www. esrl.noaa.gov/psd/. Leaf area index values are available from the National Climatic Data Center https://www.ncei.noaa.gov/data/ avhrr-land-leaf-area-index-and-fapar/access/. ORCID Daniel T. C. Cox
https://orcid.org/0000-0002-3856-3998 https://orcid.org/0000-0001-8030-9136
Ilya M. D. Maclean Alexandra S. Gardner Kevin J. Gaston
https://orcid.org/0000-0003-3817-8982
https://orcid.org/0000-0002-7235-7928
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S U P P O R T I N G I N FO R M AT I O N Additional supporting information may be found online in the Supporting Information section.
How to cite this article: Cox DTC, Maclean IMD, Gardner AS, Gaston KJ. Global variation in diurnal asymmetry in temperature, cloud cover, specific humidity and precipitation and its association with leaf area index. Glob Change Biol. 2020;00:1–13. https://doi.org/10.1111/gcb.15336